SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 800 papers

TitleStatusHype
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
Flow Exporter Impact on Intelligent Intrusion Detection Systems—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified